Project Summary/Abstract: The US Surgeon General has declared pulmonary embolism (PE) a major national health problem, causing more deaths than breast, colon, and lung cancers. The current diagnostic standard for suspected PE is CT pulmonary angiography (CTPA). However, the number of CTPA examinations is increasing dramatically, and incorrect CTPA interpretations are frequent in general practice (10-14% over/under-diagnosis). There is a clinical need to improve the efficiency and accuracy of PE diagnosis at CTPA. Our central hypothesis is that this clinical need can be addressed by exploiting computer-radiologist synergy. However, existing computer-aided diagnosis (CAD) methods for PE have serious deficiencies: they are limited in sensitivity and specificity, incapable of handling PE over-diagnosis, and operating only at the embolus level?localizing individual emboli, but PE diagnosis is rendered at the patient-level?excluding non-PE patients and dispatching PE-patients to treatment. Therefore, our objective is to overcome these deficiencies with a new methodology. We have built a strong interdisciplinary team, developed an innovative prototype, and evaluated it through our pilot clinical studies, demonstrating outstanding performance. This proposed research has three specific aims: 1) boost our current system?s embolus-level performance with our newly proposed strategies, assisting radiologists in accurately localizing emboli and facilitating precision medicine through risk stratification; 2) achieve patient-level diagnosis through our newly developed algorithms, assisting radiologists in quickly excluding negative patients and improving diagnostic efficiency; and 3) demonstrate clinical benefits of our system by testing specific clinical hypotheses. This research is innovative because (1) our approach to embolus-level detection fundamentally differs from prior approaches in that it requires no vessel segmentation, overcoming their limitations; (2) we are pioneering two uncharted areas: PE patient-level diagnosis and over-diagnosis prevention; we do not perceive any similar objectives in existing NIH grants or publications in the literature; and (3) this project utilizes our original algorithms and will yield multiple novel algorithms. Our project is significant because it (1) addresses a major national health problem; (2) develops a new methodology that transcends the current paradigm from mere detection of emboli to simultaneous patient-level diagnosis, embolus-level detection, and over-diagnosis prevention, overcoming the deficiencies of the current PE CAD systems; and (3) delivers a next- generation, high-performance PE CAD system that quickly excludes non-PE patients, accurately localizes emboli, and actively prevent PE over-diagnosis, thereby enhancing radiologists? diagnostic capabilities and supporting precision medicine through risk stratification. Successful completion of the project is expected because (1) we have already made good progress in algorithm development and clinical evaluation; (2) our approach is carefully crafted on solid algorithmic and mathematical foundations; (3) our clinical evaluation is rigorously designed; and (4) our team is uniquely capable and well prepared to conduct this project, which builds upon our innovative research in CAD, pioneering research in deformable models, and world-renowned PIOPED trials. This research is expected to have important impact on PE- related clinical practice, development of decision support systems for many diseases, and medical education.